Canadian Association for Environmental Analytical Laboratories
Bibliographic record
Abstract
Laboratory accreditation and proficiency testing are important tools used by laboratories to demonstrate competence and performance. Although it is generally accepted that accreditation and proficiency testing (PT) have helped improve the quality of testing data, there have been few studies that have objectively demonstrated this. The Canadian Association for Environmental Analytical Laboratories (CAEAL), because it operates both an accreditation program and a proficiency testing program, has long-term data that can be used to examine these issues. This paper uses long-term PT data to demonstrate that both PT participation and laboratory accreditation have a positive effect on laboratory performance. Because the CAEAL accreditation program relies on volunteer assessors from participant laboratories, an attempt is also made to determine if there is a beneficial impact to a laboratory of having a trained and active assessor on staff. Finally, a preliminary examination of an international PT study (APLAC T055 metals in water) is presented, with an emphasis on the relationship between the level of PT participation (i.e., frequency) and performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.179 | 0.070 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".